Machine learning analysis of high-repetition-rate two-dimensional Thomson scattering spectra from laser-produced plasmas

Eisenbach, S.; Mariscal, D. A.; Dorst, R. S.; Van Hoomissen, T.; Ortiz, A. M.; Zhang, H.; Pilgram, J. J.; Constantin, C. G.; Rovige, L.; Heuer, P. V.; Schaeffer, D. B.; Niemann, C.

doi:10.1088/1361-6463/ad89d6

Title: Machine learning analysis of high-repetition-rate two-dimensional Thomson scattering spectra from laser-produced plasmas

Journal Article · 29 October 2024 · Journal of Physics. D, Applied Physics

DOI: https://doi.org/10.1088/1361-6463/ad89d6 · OSTI ID:3014011

^[1];

^[2];

^[1];

^[1]; Ortiz, A. M. ^[1];

^[1];

^[3];

^[1];

^[1]

University of California, Los Angeles, CA (United States)
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
University of Rochester, NY (United States)

With the emergence of high-repetition-rate two-dimensional Thomson scattering (TS) measurements, improving spectral data analysis is a key area of interest. Here, we present a new way to derive the electron temperature and density of laser-driven blast waves in plasmas from their TS spectra with machine learning (ML). This analysis occurs in both the non-collective (α < 1) and collective (α > 1) scattering regimes with the goal of autonomously and more accurately determining T_c and n_e both where spectral data has been collected and to give the ability to predict these attributes in regions where data has not been collected. We introduce three ML models, one trained only on experimental data, one only on synthetic data, and one using transfer learning, and compare their speed and accuracy with the conventional TS inversion algorithms in the open source PlasmaPy python package.

View Accepted Manuscript (DOE)

Cite

Export

Save

Research Organization:: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States); University of Rochester, NY (United States)

Sponsoring Organization:: USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA); USDOE Office of Science (SC)

Grant/Contract Number:: AC52-07NA27344; NA0003856; NA0004033; NA0004144; NA0004147; SC0020431; SC0024549

OSTI ID:: 3014011

Report Number(s):: LLNL--JRNL-864719; 2024-+125, 2159, 3001

Journal Information:: Journal of Physics. D, Applied Physics, Journal Name: Journal of Physics. D, Applied Physics Journal Issue: 3 Vol. 58; ISSN 0022-3727; ISSN 1361-6463

Publisher:: IOP PublishingCopyright Statement

Country of Publication:: United States

Language:: English

References (44)

P3: An installation for high-energy density plasma physics and ultra-high intensity laser–matter interaction at ELI-Beamlines Weber, S.; Bechet, S.; Borneis, S. Matter and Radiation at Extremes, Vol. 2, Issue 4 https://doi.org/10.1016/j.mre.2017.03.003	journal	July 2017
Developing deep learning algorithms for inferring upstream separatrix density at JET Kit, A.; Järvinen, A. E.; Wiesen, S. Nuclear Materials and Energy, Vol. 34 https://doi.org/10.1016/j.nme.2022.101347	journal	March 2023
A platform for high-repetition-rate laser experiments on the Large Plasma Device Schaeffer, D. B.; Hofer, L. R.; Knall, E. N. High Power Laser Science and Engineering, Vol. 6 https://doi.org/10.1017/hpl.2018.11	journal	January 2018
Automated control and optimization of laser-driven ion acceleration Loughran, B.; Streeter, M. J. V.; Ahmed, H. High Power Laser Science and Engineering, Vol. 11 https://doi.org/10.1017/hpl.2023.23	journal	January 2023
Data-driven Science and Machine Learning Methods in Laser-Plasma Physics Döpp, Andreas; Eberle, Christoph; Howard, Sunny High Power Laser Science and Engineering https://doi.org/10.1017/hpl.2023.47	journal	May 2023
Measurement of the Electron Temperature by Thomson Scattering in Tokamak T3 Peacock, N. J.; Robinson, D. C.; Forrest, M. J. Nature, Vol. 224, Issue 5218 https://doi.org/10.1038/224488a0	journal	November 1969
Automation and control of laser wakefield accelerators using Bayesian optimization Shalloo, R. J.; Dann, S. J. D.; Gruse, J. -N. Nature Communications, Vol. 11, Issue 1 https://doi.org/10.1038/s41467-020-20245-6	journal	December 2020
Magnetic control of tokamak plasmas through deep reinforcement learning Degrave, Jonas; Felici, Federico; Buchli, Jonas Nature, Vol. 602, Issue 7897 https://doi.org/10.1038/s41586-021-04301-9	journal	February 2022
The Thomson scattering systems of the ASDEX upgrade tokamak Murmann, H.; Götsch, S.; Röhr, H. Review of Scientific Instruments, Vol. 63, Issue 10 https://doi.org/10.1063/1.1143504	journal	October 1992
High resolution Thomson scattering for Joint European Torus (JET) Pasqualotto, R.; Nielsen, P.; Gowers, C. Review of Scientific Instruments, Vol. 75, Issue 10 https://doi.org/10.1063/1.1787922	journal	October 2004
Development of a neural network technique for KSTAR Thomson scattering diagnostics Lee, Seung Hun; Lee, J. H.; Yamada, I. Review of Scientific Instruments, Vol. 87, Issue 11 https://doi.org/10.1063/1.4961079	journal	August 2016
Deep learning: A guide for practitioners in the physical sciences Spears, Brian K.; Brase, James; Bremer, Peer-Timo Physics of Plasmas, Vol. 25, Issue 8 https://doi.org/10.1063/1.5020791	journal	August 2018
Measuring the electron temperature and identifying plasma detachment using machine learning and spectroscopy Samuell, C. M.; Mclean, A. G.; Johnson, C. A. Review of Scientific Instruments, Vol. 92, Issue 4 https://doi.org/10.1063/5.0034552	journal	April 2021
Raster Thomson scattering in large-scale laser plasmas produced at high repetition rate Kaloyan, M.; Ghazaryan, S.; Constantin, C. G. Review of Scientific Instruments, Vol. 92, Issue 9 https://doi.org/10.1063/5.0059244	journal	September 2021
Enhanced analysis of experimental x-ray spectra through deep learning Mariscal, D. A.; Krauland, C. M.; Djordjević, B. Z. Physics of Plasmas, Vol. 29, Issue 9 https://doi.org/10.1063/5.0097777	journal	September 2022
Thomson scattering on the large plasma device Ghazaryan, S.; Kaloyan, M.; Gekelman, W. Review of Scientific Instruments, Vol. 93, Issue 8 https://doi.org/10.1063/5.0099172	journal	August 2022
A flexible proton beam imaging energy spectrometer (PROBIES) for high repetition rate or single-shot high energy density (HED) experiments (invited) Mariscal, D. A.; Djordjević, B. Z.; Anirudh, R. Review of Scientific Instruments, Vol. 94, Issue 2 https://doi.org/10.1063/5.0101845	journal	February 2023
Demonstration of a two-dimensional divertor Thomson scattering system on DIII-D Schroeder, Paul J.; Glass, Fenton; O’Gorman, Thomas Review of Scientific Instruments, Vol. 94, Issue 12 https://doi.org/10.1063/5.0173911	journal	December 2023
Two-dimensional Thomson scattering measurements of misaligned electron density and temperature gradients and associated Biermann battery produced fields Pilgram, J. J.; Constantin, C. G.; Zhang, H. Physics of Plasmas, Vol. 31, Issue 4 https://doi.org/10.1063/5.0201112	journal	April 2024
Machine Learning of Noise in LHD Thomson Scattering System Fujii, Keisuke; Yamada, Ichihiro; Hasuo, Masahiro Fusion Science and Technology, Vol. 74, Issue 1-2 https://doi.org/10.1080/15361055.2017.1396179	journal	February 2018
Progress Toward Interpretable Machine Learning–Based Disruption Predictors Across Tokamaks Rea, C.; Montes, K. J.; Pau, A. Fusion Science and Technology, Vol. 76, Issue 8 https://doi.org/10.1080/15361055.2020.1798589	journal	September 2020
Experimental investigation of the electron energy distribution function (EEDF) by Thomson scattering and optical emission spectroscopy Carbone, E. A. D.; Hübner, S.; Jimenez-Diaz, M. Journal of Physics D: Applied Physics, Vol. 45, Issue 47 https://doi.org/10.1088/0022-3727/45/47/475202	journal	November 2012
Laser light scattering in laboratory plasmas Evans, D. E.; Katzenstein, J. Reports on Progress in Physics, Vol. 32, Issue 1 https://doi.org/10.1088/0034-4885/32/1/305	journal	January 1969
Thomson scattering measurements of electron temperature and electron density in laser-driven Gd plasmas Pan, Yiming; Sunahara, Atsushi; Namba, Shinichi Journal of Physics D: Applied Physics, Vol. 56, Issue 40 https://doi.org/10.1088/1361-6463/ace36f	journal	July 2023
Accelerating the rate of discovery: toward high-repetition-rate HED science Ma, T.; Mariscal, D.; Anirudh, R. Plasma Physics and Controlled Fusion, Vol. 63, Issue 10 https://doi.org/10.1088/1361-6587/ac1f67	journal	September 2021
High temporal resolution of pedestal dynamics via machine learning on density diagnostics Ferreira, Diogo R.; Gillgren, Andreas; Ludvig-Osipov, Andrei Plasma Physics and Controlled Fusion, Vol. 66, Issue 2 https://doi.org/10.1088/1361-6587/ad15ef	journal	December 2023
A compact new incoherent Thomson scattering diagnostic for low-temperature plasma studies Vincent, Benjamin; Tsikata, Sedina; Mazouffre, Stéphane Plasma Sources Science and Technology, Vol. 27, Issue 5 https://doi.org/10.1088/1361-6595/aabd13	journal	May 2018
On the potential of physics-informed neural networks to solve inverse problems in tokamaks Rossi, Riccardo; Gelfusa, Michela; Murari, Andrea Nuclear Fusion, Vol. 63, Issue 12 https://doi.org/10.1088/1741-4326/ad067c	journal	November 2023
Machine learning-based real-time kinetic profile reconstruction in DIII-D Shousha, Ricardo; Seo, Jaemin; Erickson, Keith Nuclear Fusion, Vol. 64, Issue 2 https://doi.org/10.1088/1741-4326/ad142f	journal	December 2023
Introducing Machine-Learning in Spectroscopy for Plasma Diagnostics and Predictions Koubiti, M.; Kerebel, M. Journal of Physics: Conference Series, Vol. 2439, Issue 1 https://doi.org/10.1088/1742-6596/2439/1/012016	journal	January 2023
Laser wakefield acceleration with active feedback at 5 Hz Dann, S. J. D.; Baird, C. D.; Bourgeois, N. Physical Review Accelerators and Beams, Vol. 22, Issue 4 https://doi.org/10.1103/PhysRevAccelBeams.22.041303	journal	April 2019
Applying Deep Learning for Improving Image Classification in Nuclear Fusion Devices Farias, Gonzalo; Fabregas, Ernesto; Dormido-Canto, Sebastian IEEE Access, Vol. 6 https://doi.org/10.1109/ACCESS.2018.2881832	journal	January 2018
Solving Inverse Problems With Deep Neural Networks – Robustness Included? Genzel, Martin; Macdonald, Jan; Marz, Maximilian IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 45, Issue 1 https://doi.org/10.1109/TPAMI.2022.3148324	journal	January 2023
2022 Review of Data-Driven Plasma Science Anirudh, Rushil; Archibald, Rick; Asif, M. Salman IEEE Transactions on Plasma Science https://doi.org/10.1109/TPS.2023.3268170	journal	January 2023
High average power, diode pumped petawatt laser systems: a new generation of lasers enabling precision science and commercial applications Haefner, C. L.; Bayramian, A.; Betts, S. SPIE Optics + Optoelectronics, SPIE Proceedings https://doi.org/10.1117/12.2281050	conference	June 2017
Automated repair of laser damage on National Ignition Facility optics using machine learning Trummer, Scott; Larkin, Glenn R.; Mascio-Kegelmeyer, Laura Laser-Induced Damage in Optical Materials 2018: 50th Anniversary Conference https://doi.org/10.1117/12.2501826	conference	November 2018
Application of Machine Learning to Determine Electron Temperature in Globus-M2 Tokamak Using the Soft X-Ray Emission Data and the Thomson Scattering Diagnostics Data Tkachenko, E. E.; Kurskiev, G. S.; Zhiltsov, N. S. Physics of Atomic Nuclei, Vol. 85, Issue 7 https://doi.org/10.1134/S1063778822070122	journal	December 2022
ICAN as a new laser paradigm for high energy, high average power femtosecond pulses Brocklesby, W. S.; Nilsson, J.; Schreiber, T. The European Physical Journal Special Topics, Vol. 223, Issue 6 https://doi.org/10.1140/epjst/e2014-02172-4	journal	May 2014
Plasma Scattering of Electromagnetic Radiation: Theory and Measurement Techniques Froula, Dustin H.; Glenzer, Siegfried H.; Luhmann, Neville C. Fusion Science and Technology, Vol. 61, Issue 1 https://doi.org/10.13182/FST12-A13342	journal	January 2012
First Results from the Thomson Scattering Diagnostic on the Large Plasma Device Kaloyan, Marietta; Ghazaryan, Sofiya; Tripathi, Shreekrishna P. Instruments, Vol. 6, Issue 2 https://doi.org/10.3390/instruments6020017	journal	April 2022
Two-Dimensional Thomson Scattering in Laser-Produced Plasmas Zhang, Haiping; Pilgram, Jessica J.; Constantin, Carmen G. Instruments, Vol. 7, Issue 3 https://doi.org/10.3390/instruments7030025	journal	September 2023
Deep Learning Models to Reduce Stray Light in TJ-II Thomson Scattering Diagnostic Correa, Ricardo; Farias, Gonzalo; Fabregas, Ernesto Sensors, Vol. 24, Issue 9 https://doi.org/10.3390/s24092764	journal	April 2024
Laboratory Observations of Ultra-low-frequency Analog Waves Driven by the Right-hand Resonant Ion Beam Instability Heuer, Peter V.; Weidl, Martin. S.; Dorst, Robert S. The Astrophysical Journal, Vol. 891, Issue 1 https://doi.org/10.3847/2041-8213/ab75f4	journal	February 2020
2DTS dataset for ML analysis Eisenbach, S.; Mariscal, D.; Dorst, R. Zenodo https://doi.org/10.5281/zenodo.13800034	dataset	January 2024

Similar Records

Transfer learning for analysis of collective and non-collective Thomson scattering spectra

Journal Article · 2026 · Physics of Plasmas · OSTI ID:3013050

Van Hoomissen, T.; Alhuthali, J.; Ortiz, A. M.; +9 more

Two-Dimensional Thomson Scattering in Laser-Produced Plasmas

Journal Article · 2023 · Instruments · OSTI ID:1998795

Zhang, Haiping; Pilgram, Jessica J.; Constantin, Carmen G.; +7 more

Raster Thomson scattering in large-scale laser plasmas produced at high repetition rate

Journal Article · 2021 · Review of Scientific Instruments · OSTI ID:1850372

Kaloyan, M.; Ghazaryan, S.; Constantin, C. G.; +5 more

Related Subjects

70 PLASMA PHYSICS AND FUSION TECHNOLOGY
Physics - Plasma physics
Thomson scattering
laser-produced plasma
machine learning

Title: Machine learning analysis of high-repetition-rate two-dimensional Thomson scattering spectra from laser-produced plasmas

Citation Formats

References (44)

Similar Records

Related Subjects